Supplementary Material for "Sound and Meaning in Auditory Data Display"

Auditory data display is an interdisciplinary field linking auditory perception research, sound engineering, data mining, and human-computer interaction in order to make semantic contents of data perceptually accessible in the form of (nonverbal) audible sound. For this goal it is important to understand the different ways in which sound can encode meaning. We discuss this issue from the perspectives of language, music, functionality, listening modes, and physics, and point out some limitations of current techniques for auditory data display, in particular when targeting high-dimensional data sets. As a promising, potentially very widely applicable approach, we discuss the method of model-based sonification (MBS) introduced recently by the authors and point out how its natural semantic grounding in the physics of a sound generation process supports the design of sonifications that are accessible even to untrained, everyday listening. We then proceed to show that MBS also facilitates the design of an intuitive, active navigation through "acoustic aspects", somewhat analogous to the use of successive two-dimensional views in three-dimensional visualization. Finally, we illustrate the concept with a first prototype of a "tangible" sonification interface which allows us to "perceptually map" sonification responses into active exploratory hand motions of a user, and give an outlook on some planned extensions. ### Table 1: Sound examples for the particle trajectory sonification model + Single Particle in a single data point potential function phi(r): [S1 (mp3)](https://pub.uni-bielefeld.de/download/2703095/2703096) + Set of 50 particles in the potential function for a 2D dataset with one cluster: [S2 (mp3)](https://pub.uni-bielefeld.de/download/2703095/2703097) --- and with three clusters: [S3 (mp3)](https://pub.uni-bielefeld.de/download/2703095/2703098) + Sequence of particle trajectory sonifications, systematically decreasing the bandwidth sigma, starting with large values, where V contains only one trough at the mean of all data, ending with sigma so small that all data point potential functions are well separated. Listen to the examples for a dataset with a gaussian distribution of data points: [S4 (mp3)](https://pub.uni-bielefeld.de/download/2703095/2703099) and with a mixture of 3 gaussian distributions with different mean and covariance [S5 (mp3)](https://pub.uni-bielefeld.de/download/2703095/2703100)